Solving the undirected feedback vertex set problem by local search

TL;DR

This paper introduces a simulated annealing-based local search algorithm for the undirected feedback vertex set problem, effectively reducing cycle intersections in large graphs and performing comparably to belief propagation methods.

Contribution

The paper presents a novel local search heuristic using simulated annealing for the undirected FVS problem, replacing global cycle constraints with local vertex order constraints.

Findings

Algorithm performs well on large random graphs

Comparable to belief propagation-guided decimation

Effective in reducing feedback vertex set size

Abstract

An undirected graph consists of a set of vertices and a set of undirected edges between vertices. Such a graph may contain an abundant number of cycles, then a feedback vertex set (FVS) is a set of vertices intersecting with each of these cycles. Constructing a FVS of cardinality approaching the global minimum value is a optimization problem in the nondeterministic polynomial-complete complexity class, therefore it might be extremely difficult for some large graph instances. In this paper we develop a simulated annealing local search algorithm for the undirected FVS problem. By defining an order for the vertices outside the FVS, we replace the global cycle constraints by a set of local vertex constraints on this order. Under these local constraints the cardinality of the focal FVS is then gradually reduced by the simulated annealing dynamical process. We test this heuristic algorithm on large instances of Er\"odos-Renyi random graph and regular random graph, and find that this algorithm is comparable in performance to the belief propagation-guided decimation algorithm.